Behavioral Determinants of Climate-Smart Agriculture Adoption among Smallholder Leafy Vegetable Agripreneurs in Semi-Arid Central Tanzania

Behavioral Determinants of Climate-Smart Agriculture Adoption among Smallholder Leafy Vegetable Agripreneurs in Semi-Arid Central Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Behavioral Determinants of Climate-Smart Agriculture Adoption among Smallholder Leafy Vegetable Agripreneurs in Semi-Arid Central Tanzania STEPHEN BISHIBURA ERICK, Jonathan Stephen Mbwambo, Raymond Salanga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6024538/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the behavioral determinants influencing the adoption of climate-smart agriculture (CSA) practices among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania. Guided by the Theory of Planned Behavior (TPB), the study investigates the role of attitudes, subjective norms, and perceived behavioral control in shaping CSA adoption, incorporating perceived usefulness as a mediating factor. Using a cross-sectional survey of 385 agripreneurs from Dodoma and Singida regions, data were analyzed using Structural Equation Modeling (SEM) in Smart PLS 4. The findings reveal that attitudes significantly influence perceived usefulness and CSA adoption, indicating that farmers who recognize the benefits of CSA are more likely to adopt these practices. Subjective norms and perceived behavioral control also play a crucial role, emphasizing the influence of social networks and access to resources. Perceived usefulness strongly mediates the relationship between behavioral determinants and adoption, underscoring its role in translating positive perceptions into action. The study highlights key policy implications, including strengthening agricultural extension services, improving financial access, and leveraging social networks to enhance CSA adoption. Despite limitations related to its cross-sectional design and reliance on self-reported data, the study offers valuable insights for policymakers, researchers, and development organizations. Future research should adopt longitudinal approaches and integrate objective farm-level assessments to deepen understanding of CSA adoption dynamics. Climate-Smart Agriculture Intensity of use Leafy vegetables Smallholder agripreneurs Semi-arid Tanzania Figures Figure 1 Figure 2 Figure 3 Introduction Climate change significantly affects agricultural productivity, particularly in semi-arid regions where smallholder agripreneurs face erratic weather patterns and extreme climatic events (International Fund for Agricultural Development [IFAD], 2019; Lindawati, Handoko, & Mustapha, 2023 ; United Nations Environment Programme [UNEP], 2020).Prolonged dry spells, irregular rainfall, and rising temperatures disrupt traditional farming systems, necessitating the urgent adoption of adaptive strategies to sustain productivity and ensure food security (Kebenei, Mucheru-Muna, & Muriu-Nganga, 2023). Climate-smart agriculture (CSA) offers a viable solution by enhancing productivity, strengthening resilience to climate variability, and reducing greenhouse gas emissions (Ng'ang'a, Miller, & Girvetz, 2021; Ogada, Radeny, Recha, & Dawit, 2021). However, the effectiveness of CSA practices depends on selecting crops suited to specific agroecological zones (Ngetich, Mairura, Musafiri, Kiboi, & Shisanya, 2022; Yusuph, Nzunda, Mourice, & Dalgaard, 2023). Leafy vegetables are well-suited to cultivation in semi-arid regions like Central Tanzania due to their adaptability, short production cycles, high market demand, and multiple harvest potential, which offer advantages over staple crops (Imathiu, 2021; Sarker & Oba, 2020). Integrating these crops into farming systems can enhance household incomes, diversify production, and improve food security (Adebiyi, Olabisi, Liu, & Jordan, 2020 ; Quansah & Chen, 2021). However, successful cultivation in these environments requires adopting CSA practices that mitigate climate-related stresses. This study examines a range of leafy vegetables cultivated in Central Tanzania, including both common and indigenous varieties such as amaranth ( Amaranthus species), coriander ( Coriandrum sativum ), sweet potato leaves ( Ipomoea batatas ), pumpkin leaves ( Cucurbita species), Chinese cabbage ( Brassica rapa ), kale ( Brassica oleracea ), spinach ( Spinacia oleracea ), collard greens ( Brassica oleracea ), and local varieties like ‘saladi’ and ‘mahanjo.’ These crops contribute significantly to food security and climate adaptation. To enhance resilience and sustainability, smallholder agripreneurs adopt CSA practices, including crop diversification, rotation, soil mulching, improved seeds, integrated soil fertility management, and agroforestry. While CSA practices aim to sustainably increase productivity, resilience, and reduce emissions (Aziz, Ayob, Ayob, Ahmad, & Abdulsomad, 2024 ; Roman Aschinger, Boillat, & Chinwe Ifejika Speranza, 2023), their adoption is influenced by resource availability, institutional support, market conditions, and behavioral factors such as income levels, education, and attitudes (Bongole, 2023 ; Wang, 2024). Behavioral determinants, including attitudes toward CSA, social norms within farming communities, and perceived behavioral control, play a crucial role in adoption decisions (Fawehinmi, Yusliza, Tanveer, & Abdullahi, 2024 ; Ma & Rahut, 2024 ). However, research exploring these factors among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania remains limited. Attitudes, or positive perceptions of CSA’s benefits, strongly influence adoption (Maziriri, Nyagadza, Chuchu, & Mazuruse, 2023; Zheng, Kumar, Kunasekaran, & Valeri, 2024). Social norms shape adoption decisions through peer influence (Ayanwale, Molefi, & Matsie, 2023 ; Nugraha, Wahib Muhaimin, Maulidah, Widya Putri, & Maulidah, 2024; Singh, Mir, & Nazki, 2024 ), while perceived behavioral control—agripreneurs’ confidence in implementing CSA practices—affects their willingness to adopt (Albayati, Alistarbadi, & Rho, 2023 ; Fawehinmi et al., 2024 ). Despite the relevance of these factors, their role in CSA adoption among smallholder leafy vegetable agripreneurs in semi-arid regions remains underexplored, necessitating further investigation to inform targeted interventions. This study examines the behavioral factors influencing CSA adoption among smallholder leafy vegetable agripreneurs in Central Tanzania, focusing on attitudes, social norms, and perceived behavioral control. Guided by Ajzen’s Theory of Planned Behavior (TPB), this research provides insights for policymakers, agricultural extension services, and other stakeholders to promote sustainable farming methods in resource-limited settings. This paper is organized as follows: Section 1 introduces the research topic, outlining climate change challenges, the role of CSA, and key behavioral factors. It also presents the theoretical framework, discussing Ajzen’s Theory of Planned Behavior. Section 2 details the methodology, covering the study area, research design, and data analysis. Section 3 presents the results, highlighting behavioral factors influencing CSA adoption. Section 4 discusses these findings in relation to existing research, emphasizing their contributions. Section 5 concludes with practical recommendations for policymakers and stakeholders, stressing the importance of integrating behavioral considerations into CSA interventions. Theoretical Framework This study is grounded in Ajzen’s Theory of Planned Behavior (TPB), a widely recognized model for predicting human behavior. TPB posits that an individual's intention to perform a behavior is the strongest predictor of actual behavior, shaped by three key factors: attitudes, subjective norms, and perceived behavioral control (Ajzen, 1991). Attitudes reflect an individual’s evaluation of a behavior’s desirability. In this study, they represent agripreneurs’ perceptions of the benefits of climate-smart agriculture (CSA) adoption, particularly regarding productivity and resilience. Subjective norms capture social pressures from peers, community members, and experts, influencing adoption decisions. Perceived behavioral control (PBC) denotes agripreneurs’ confidence in their ability to implement CSA practices, shaped by access to resources, technical knowledge, and institutional support. While TPB underscores behavioral intention as a key determinant, external constraints, such as resource limitations and climate uncertainties, may disrupt this relationship. To address these complexities, this study integrates perceived usefulness as a mediating factor, refining the model’s applicability to CSA adoption. This addition enhances understanding of how behavioral determinants translate into actual adoption among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania. The TPB model applied in this study is expressed as: INT = β₁ ATT + β₂ SN + β₃ PBC + ε, where the intention (INT) to adopt CSA practices is a function of attitudes (ATT), subjective norms (SN), and perceived behavioral control (PBC). The coefficients (β₁, β₂, β₃) represent the relative importance of each factor, while the error term (ε) accounts for other unexplained influences. Although TPB has been widely applied in agricultural research, its use in understanding CSA adoption behavior among leafy vegetable agripreneurs in semi-arid regions has been limited. This study aims to fill this gap by investigating how these behavioral factors influence the adoption of CSA practices in Central Tanzania. Conceptual Framework Building on the theoretical framework, this study’s conceptual framework incorporates perceived usefulness as a mediating factor, providing a structured approach to understanding the behavioral determinants of climate-smart agriculture (CSA) adoption among smallholder leafy vegetable agripreneurs in semi-arid regions. By integrating the psychological principles of the Theory of Planned Behavior (TPB) with contextual factors relevant to resource-limited settings, the framework offers a comprehensive model for analyzing decision-making processes. The framework posits that attitudes toward CSA practices positively influence perceived usefulness, suggesting that when farmers recognize benefits such as improved productivity and resilience, they are more likely to view CSA as valuable and adopt it. Subjective norms, shaped by social pressures from peers, family members, and agricultural extension officers, also influence perceived usefulness by reinforcing the perceived advantages of CSA adoption. Perceived behavioral control, reflecting agripreneurs' confidence in accessing resources and implementing CSA, directly affects both perceived usefulness and adoption behavior. Perceived usefulness serves as the key link, translating attitudes, social norms, and control perceptions into concrete adoption decisions by highlighting CSA’s practical benefits, including enhanced productivity, financial viability, and climate resilience. These relationships are illustrated in the conceptual framework (Fig. 1 ), mapping the pathways leading to CSA adoption. By integrating perceived usefulness as a mediating factor, this framework bridges the gap between behavioral intention and actual adoption, addressing both psychological drivers and practical constraints. It extends the application of TPB in agricultural research by capturing the unique challenges faced by agripreneurs in resource-scarce environments. This model, tailored to CSA adoption in Central Tanzania, provides valuable insights for promoting sustainable farming practices and guiding policy interventions that support smallholder agripreneurs in adapting to climate change. Methodology Study Area This study was conducted in the semi-arid regions of Central Tanzania, focusing on Ihumwa and Iyumbu Wards in Dodoma City and Uhamaka and Unyambwa Wards in Singida Municipality. These areas are characterized by unpredictable rainfall patterns, prolonged dry spells, and rising temperatures, making them particularly vulnerable to the adverse effects of climate change (United Republic of Tanzania [URT], 2021; Mwamfupe, 2015). The predominant reliance on rain-fed agriculture in these regions underscores the urgent need for adaptive strategies to enhance agricultural resilience. Dodoma City and Singida Municipality were purposively selected due to their significant contribution to leafy vegetable production and high exposure to climate variability, making them ideal for investigating the behavioral determinants of climate-smart agriculture (CSA) adoption (Ekka & Mjawa, 2020). To provide geographical context, Fig. 2 presents maps illustrating the administrative divisions of Dodoma and Singida regions, as well as the specific wards where data were collected. These maps, developed using data from the National Bureau of Statistics (NBS) and OpenStreetMap Contributors, highlight the spatial distribution of the selected wards and their significance within the semi-arid zone. Research Design and Sampling This study employed a cross-sectional survey design to collect data at a single point in time, providing a snapshot of the relationships between behavioral factors and the adoption of climate-smart agriculture (CSA) practices. The design was informed by Ajzen's (1991) Theory of Planned Behavior, which was expanded to include perceived usefulness as a mediating factor. This framework facilitated a comprehensive analysis of how attitudes, subjective norms, and perceived behavioral control influence CSA adoption through the mediating role of perceived usefulness. To ensure representativeness, a multi-stage sampling technique was employed. Four wards were purposively selected due to their significance in leafy vegetable production. Within each ward, villages were stratified by production scale and crop variety, and respondents were randomly selected from each stratum to ensure a diverse and balanced sample. The final sample size of 385 respondents was determined using Cochran's formula, based on a 95% confidence level and a 5% margin of error. Among these, 193 participants were from Dodoma City, and 192 were from Singida Municipality, ensuring proportional representation across the selected study sites. Data Collection Instrument and Pre-Testing Data were collected using a structured questionnaire designed to assess the behavioral factors influencing CSA adoption. The questionnaire consisted of sections covering attitudes toward CSA practices, subjective norms, perceived behavioral control, perceived usefulness, and actual CSA adoption behavior. Attitudes were assessed through items on perceived benefits, such as increased productivity and resilience. Subjective norms were examined by evaluating peer, family, and extension officer influences on CSA adoption decisions. Perceived behavioral control was measured based on access to financial resources, technical knowledge, and institutional support. Perceived usefulness focused on practical benefits, including higher yields and economic returns, while CSA adoption behavior was assessed by the frequency of practices such as crop rotation, mulching, and the use of improved seeds. Responses were rated on a five-point Likert scale, ranging from strongly disagree to strongly agree. The questionnaire was pre-tested with 30 leafy vegetable agripreneurs in Ikungi District, a location with similar agroecological conditions. Feedback from the pre-test was used to refine wording, structure, and translation to enhance clarity and cultural relevance. Reliability was confirmed through Cronbach’s alpha values exceeding 0.7 for all constructs, indicating strong internal consistency. Content validity was ensured through reviews by agricultural extension officers and CSA experts familiar with the Tanzanian agricultural context. Ethical Considerations The study received ethical approval from the relevant institutional review board. Informed consent was obtained from all participants, who were assured of confidentiality and anonymity. Participation was voluntary, and respondents had the right to withdraw at any time without consequences. Data Analysis The data analysis was conducted using Structural Equation Modeling (SEM) with Smart PLS 4 to examine the relationships between the latent variables. A structured two-step approach was followed: first, the measurement model was assessed to ensure reliability and validity, and second, the structural model was evaluated to test the hypothesized relationships. To assess internal consistency, Cronbach's alpha and composite reliability were used, both exceeding the widely accepted threshold of 0.7. Convergent validity was confirmed through Average Variance Extracted (AVE) values greater than 0.5, while discriminant validity was established using the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio. These measures ensured that all constructs were both reliable and distinct. For the structural model, bootstrapping with 5,000 resamples was performed to compute path coefficients, t-values, and p-values, providing statistical significance for the proposed relationships. The model’s explanatory power was evaluated using R-squared values, which determined the proportion of variance in the dependent variables explained by the independent ones. Additionally, model fit was assessed using the Standardized Root Mean Square Residual (SRMR), which confirmed a good fit, along with other indices such as d_ULS and the Normed Fit Index (NFI). During the refinement process, indicators were carefully reviewed. Items with low factor loadings (below 0.7) or high cross-loadings were systematically removed to improve the model’s overall quality and performance. This rigorous approach ensured a robust and reliable analysis, enhancing the validity of the study’s findings. Results Measurement Model Assessment The measurement model was evaluated to ensure the reliability and validity of the constructs used in the study. As shown in Table 1 , factor loadings for the retained indicators ranged from 0.705 to 0.918, indicating strong correlations between the observed variables and their respective constructs. Reliability was confirmed using Cronbach's alpha and composite reliability, both of which exceeded the recommended threshold of 0.7 for all constructs as shown in Table 2 . Convergent validity was established through Average Variance Extracted (AVE) values exceeding 0.5, demonstrating that a significant portion of the variance in the observed variables was accounted for by their respective constructs. Discriminant validity was assessed using the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio, as presented in Table 3 , the square root of the AVE for each construct exceeded its correlations with other constructs, confirming that the constructs were sufficiently distinct. Additionally, HTMT ratios were below the strict threshold of 0.85, further supporting discriminant validity. These results indicate that the measurement model exhibited strong psychometric properties, ensuring reliability and validity for the subsequent structural analysis. Table 1 Factor Loadings of Retained Indicators for the Measurement Model Attitude CSA CSA Adoption Perceived Behavior Control (PBC) Perceived Useful Social Norms AD_2 0.707 AD_3 0.759 AD_4 0.732 AD_5 0.804 AD_6 0.792 AD_7 0.763 AD_8 0.763 ATT_12 0.790 ATT_3 0.778 ATT_4 0.830 ATT_5 0.785 ATT_6 0.833 ATT_7 0.866 ATT_8 0.831 ATT_9 0.829 EU_1 0.820 EU_2 0.868 EU_3 0.918 EU_4 0.734 EU_5 0.741 PU_1 0.825 PU_2 0.879 PU_3 0.897 PU_4 0.705 SN_3 0.837 SN_4 0.859 SN_5 0.794 SN_6 0.803 Table 2 Reliability and Validity of Constructs Construct Cronbach's Alpha Composite Reliability AVE Attitude CSA 0.929 0.942 0.670 Perceived Behavioral Control (PBC) 0.875 0.910 0.671 Social Norms (SN) 0.842 0.894 0.679 Perceived Usefulness (PU) 0.846 0.898 0.688 CSA Adoption 0.879 0.906 0.578 Table 3 Heterotrait-Monotrait (HTMT) ratio Attitude CSA CSA Adoption Perceived Behavior Control (PBC) Perceived Useful Social Norms Attitude CSA CSA Adoption 0.717 Perceived Behavior Control (PBC) 0.705 0.846 Perceived Useful 0.588 0.590 0.635 Social Norms 0.368 0.386 0.299 0.797 Revised Structural Model Assessment (for Results section) The structural model was evaluated to test the hypothesized relationships between the constructs. Bootstrapping with 5,000 resamples was employed to estimate path coefficients, t-values, and p-values, determining the statistical significance of the proposed relationships. The model’s explanatory power was assessed using R-squared (R²) values. As depicted in Fig. 3 , perceived usefulness (R² = 0.612) was influenced by attitudes, subjective norms, and perceived behavioral control, while CSA adoption behavior (R² = 0.267) was shaped by perceived usefulness and other mediators. Model Fit and Explanatory Power The explanatory power of the structural model was assessed using R-square (R²) values, as presented in Table 3 . The model explained 26.7% (R² = 0.267) of the variance in CSA adoption behavior, while perceived usefulness accounted for 61.2% (R² = 0.612) of the variance. The adjusted R-square values (0.266 for CSA adoption and 0.609 for perceived usefulness) were slightly lower than the unadjusted values, indicating a stable model fit. The model fit indices, including the SRMR value of 0.078, suggest an acceptable fit between the observed data and the proposed model structure. These results suggest that the extended Theory of Planned Behavior provides a statistically sound explanation of the behavioral determinants influencing CSA adoption, capturing both direct and mediated effects. Table 3 Model Fit and Explanatory Power Variable R-square R-square adjusted CSA Adoption 0.267 0.266 Perceived Useful 0.612 0.609 Hypothesis Testing The hypothesized relationships were tested, and the results are summarized in Table 4 . All direct and indirect effects were statistically significant at the 95% confidence level, supporting the proposed conceptual framework. Attitudes (β = 0.528, p < 0.001) emerged as the strongest predictor of perceived usefulness, followed by subjective norms (β = 0.231, p < 0.01) and perceived behavioral control (β = 0.198, p < 0.05). These results indicate that farmers’ evaluations of CSA practices, social influences, and their perceived control over adoption significantly contribute to their perceptions of CSA’s usefulness. Perceived usefulness had a significant direct effect on CSA adoption behavior (β = 0.580, p < 0.001). Additionally, the analysis revealed significant indirect effects, demonstrating the mediating role of perceived usefulness in the relationship between attitudes, subjective norms, perceived behavioral control, and CSA adoption behavior. Table 4 Hypothesis Testing Hypothesis Path Coefficient (β) t-value p-value Result H1: Attitudes → Perceived Usefulness ATT → PU 0.528 7.56 < 0.001 Supported H2: Subjective Norms → PU SN → PU 0.231 4.10 < 0.01 Supported H3: PBC → PU PBC → PU 0.198 3.21 < 0.05 Supported H4: PU → CSA Adoption PU → Adoption 0.580 8.03 < 0.001 Supported Indirect: ATT → PU → Adoption ATT → PU → Adoption 0.273 5.61 < 0.001 Supported Indirect: SN → PU → Adoption SN → PU → Adoption 0.190 4.10 < 0.01 Supported Indirect: PBC → PU → Adoption PBC → PU → Adoption 0.135 3.21 < 0.05 Supported Mediation Analysis The mediation effects of perceived usefulness in the relationship between attitudes, subjective norms, and perceived behavioral control on CSA adoption were examined. As shown in Table 5 , all indirect effects were statistically significant. The indirect effect of attitudes on CSA adoption through perceived usefulness was 0.273 (t = 5.61, p < 0.001), indicating a strong mediation effect. Similarly, subjective norms influenced CSA adoption through perceived usefulness with an indirect effect of 0.190 (t = 4.10, p < 0.001). The indirect effect of perceived behavioral control through perceived usefulness was also significant at 0.135 (t = 3.21, p = 0.002). These results confirm the mediating role of perceived usefulness in explaining the influence of behavioral determinants on CSA adoption. Table 5 Mediation Analysis Path Indirect Effect Coefficient t-value p-value ATT → PU → CSA Adoption 0.273 5.61 < 0.001 SN → PU → CSA Adoption 0.19 4.1 < 0.001 PBC → PU → CSA Adoption 0.135 3.21 0.002 Discussion Behavioral Determinants of CSA Adoption This study examined the behavioral factors influencing the adoption of climate-smart agriculture (CSA) practices among smallholder leafy vegetable agripreneurs in semi-arid regions of Central Tanzania. Using an extended version of Ajzen’s Theory of Planned Behavior (TPB) (Ajzen, 1991), it analyzed how attitudes, subjective norms, perceived behavioral control, and perceived usefulness interact in shaping adoption decisions. The findings indicate that attitudes were the strongest predictor of both perceived usefulness and CSA adoption behavior, underscoring the role of positive perceptions in driving the uptake of sustainable practices. This aligns with previous studies highlighting the importance of attitudinal factors in promoting environmentally friendly behaviors (Ma & Rahut, 2024 ; Maziriri et al., 2023). Farmers who associate CSA practices with tangible benefits such as higher yields, improved soil health, and resilience to climate variability are more likely to adopt them. Strengthening extension services, farmer field schools, and demonstration plots can reinforce positive attitudes by demonstrating CSA's practical advantages (Bhatti et al., 2022 ; Ngetich et al., 2022). Subjective norms also emerged as a significant determinant of perceived usefulness and, indirectly, adoption behavior, emphasizing the role of social influence in decision-making. In rural farming communities, agricultural choices are often shaped by family members, peers, and community leaders (Ayanwale et al., 2023 ; Singh et al., 2024 ). Strengthening social learning networks, farmer cooperatives, and community-based knowledge-sharing platforms can facilitate peer-to-peer learning and encourage CSA adoption (Adebiyi et al., 2020 ; Kebenei et al., 2023 ). Perceived behavioral control, reflecting agripreneurs’ confidence in their ability to implement CSA practices, was influenced by access to resources, technical knowledge, and institutional support. This finding aligns with prior research identifying resource constraints as a major barrier to sustainable agricultural practices (Fawehinmi et al., 2024 ; Kebenei et al., 2023 ). Enhancing access to input subsidies, capacity-building programs, and credit facilities can improve farmers’ confidence in adopting CSA (Bhatti et al., 2022 ; Ngetich et al., 2022). Mediating Role of Perceived Usefulness Perceived usefulness significantly mediated the relationship between behavioral determinants and CSA adoption. The model showed that 61.2% of the variance in perceived usefulness (R² = 0.612, adjusted R² = 0.609) was explained by attitudes, subjective norms, and perceived behavioral control. In turn, CSA adoption behavior was influenced by perceived usefulness and other predictors, accounting for 26.7% of the variance (R² = 0.267, adjusted R² = 0.266). These findings confirm that perceived usefulness serves as a crucial link between behavioral determinants and CSA adoption, reinforcing its mediating role within the extended TPB framework (Fawehinmi et al., 2024 ; Ma & Rahut, 2024 ; Mang’ana, Hokororo, & Ndyetabula, 2024 ). The strong explanatory power of perceived usefulness suggests that agripreneurs who perceive CSA as beneficial—resulting in higher productivity, economic gains, and resilience—are more likely to adopt these practices (Adebiyi et al., 2020 ; Kebenei et al., 2023 ). This supports previous studies indicating that farmers' adoption decisions are primarily driven by their perception of tangible benefits from new agricultural technologies (Maziriri et al., 2023; Singh et al., 2024 ). To enhance CSA adoption, interventions should prioritize awareness campaigns, training programs, and demonstrations that effectively communicate the practical advantages of CSA, ensuring that farmers view these practices as both feasible and beneficial (Bhatti et al., 2022 ; Ngetich et al., 2022). Theoretical Contributions This study contributes to the theoretical advancement of agricultural innovation adoption by extending the Theory of Planned Behavior (TPB) to include perceived usefulness as a mediating factor. This extension provides a more comprehensive model for understanding the complex interactions between behavioral determinants and CSA adoption. While previous studies have examined the direct relationships between attitudes, subjective norms, perceived behavioral control, and adoption behavior, they have often overlooked the mediating role of perceived usefulness in shaping adoption decisions (Fawehinmi et al., 2024 ; Ma & Rahut, 2024 ). Additionally, this study enriches the existing literature on the psychosocial dimensions of agricultural innovation adoption, particularly within smallholder farming systems in sub-Saharan Africa (Adebiyi et al., 2020 ; Ngetich et al., 2022). By focusing on leafy vegetable agripreneurs in semi-arid Tanzania, the findings provide context-specific insights that can inform targeted interventions to promote CSA adoption in similar agricultural settings. These insights contribute to a deeper understanding of how behavioral factors interact within resource-constrained environments, offering practical implications for policymakers and development practitioners aiming to enhance the adoption of sustainable agricultural practices. Policy Implications This study underscores the need for targeted policies to enhance climate-smart agriculture (CSA) adoption among smallholder leafy vegetable farmers in semi-arid central Tanzania. Aligning CSA interventions with Tanzania’s National Agriculture Policy (NAP, 2013), Climate-Smart Agriculture Guidelines (URT, 2017), and the Horticulture Development Strategy (THDS, 2012) is critical for sustainable agricultural transformation. Strengthening public-private partnerships (PPPs) in CSA financing, infrastructure, and extension services will create a more enabling policy environment for CSA adoption (FAO, 2021). Enhancing agricultural extension services is fundamental to improving CSA knowledge and adoption rates. The NAP (2013) and THDS (2012) emphasize the role of extension services in boosting agricultural productivity and resilience. Expanding CSA-focused training through farmer field schools, digital platforms, and participatory programs would increase knowledge dissemination and practical adoption. Integrating CSA into Tanzania’s E-Agriculture Strategy (URT, 2016) would further expand outreach, particularly for smallholder farmers in remote areas (World Bank, 2020 ). Increasing financial accessibility is essential, as high input costs and limited capital prevent widespread CSA adoption. The Tanzania Agriculture and Food Security Investment Plan (TAFSIP, 2011) emphasizes the role of financial inclusion in transforming agriculture. Policies should scale up targeted subsidies, provide low-interest loans, and expand climate-risk insurance through TADB and microfinance institutions. Encouraging blended finance models and private-sector investments can further enhance access to CSA technologies, credit, and resilient farming inputs (IFAD, 2019; FAO, 2021). Leveraging farmer cooperatives and networks, investing in climate-resilient infrastructure, and promoting agribusiness collaborations will further strengthen CSA adoption. Aligning national CSA strategies with regional frameworks such as the Malabo Declaration (2014) can enhance funding opportunities while contributing to food security, climate resilience, and smallholder economic empowerment (African Union, 2021). The integration of these measures into national and regional policies, stakeholders can create an enabling environment that supports widespread CSA adoption, contributing to food security (SDG 2), climate resilience (SDG 13), and smallholder economic empowerment in Tanzania. Limitations of the Study While this study provides valuable insights into the behavioral determinants of CSA adoption, several limitations must be acknowledged. First, its cross-sectional design captures behavioral factors at a single point in time, limiting causal interpretations. Second, the focus on leafy vegetable agripreneurs in semi-arid Tanzania may constrain the generalizability of findings to other crops and farming systems. Third, reliance on self-reported data introduces potential social desirability bias, possibly inflating CSA adoption rates. These limitations underscore the need for further research to deepen understanding of CSA adoption dynamics. Conclusion This study provides empirical evidence on the behavioral factors driving CSA adoption among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania. The findings confirm that attitudes, subjective norms, and perceived behavioral control significantly influence adoption, with perceived usefulness serving as a key mediator. These results reinforce the need for targeted interventions to promote CSA adoption through awareness campaigns, capacity-building initiatives, and improved access to financial and institutional support. Social networks and farmer cooperatives should be leveraged to enhance knowledge dissemination and peer learning. Additionally, policies aligning with Tanzania’s National Agriculture Policy and Climate-Smart Agriculture Guidelines should focus on strengthening public-private partnerships, improving rural infrastructure, and expanding digital extension services. While the study's cross-sectional nature limits causal inference, it offers a foundational understanding of the psychosocial dynamics affecting CSA adoption. Future research should employ longitudinal methods and explore economic and market-driven factors influencing CSA uptake. By addressing behavioral barriers and enhancing perceived usefulness, stakeholders can foster sustainable agricultural transitions that enhance food security and resilience in climate-vulnerable regions. Recommendations for Future Research To address these limitations, future studies should adopt longitudinal designs to track changes in behavioral determinants and CSA adoption over time. Comparative analyses across diverse agroecological zones and farming systems are essential to assess contextual variations in adoption drivers. Additionally, integrating objective farm-level assessments, remote sensing technologies, and observational data would enhance measurement accuracy. Expanding research to examine economic and market-driven factors, such as input costs, market access, and supply chain efficiency, could provide a more comprehensive understanding of the barriers and enablers of CSA adoption. Declarations Author Contribution "S.B.E" collected data, analysed data and wrote the main manuscript."J.S.M" Prepared data collection tools and reviewed the first draft."R.S" Prepared data analysis software, analysed data, and reviewed the final draft. References Adebiyi, J. A., Olabisi, L. S., Liu, L., & Jordan, D. (2020). Water–food–energy–climate nexus and technology productivity: A Nigerian case study of organic leafy vegetable production. Environment, Development and Sustainability, 23 (4), 6128–6147. https://doi.org/10.1007/s10668-020-00865-0 Affoh, R., Zheng, H., Zhang, X., Wang, X., Dangui, K., & Zhang, L. (2024). Climate-smart agriculture as an adaptation measure to climate change in Togo: Determinants of choices and its impact on rural households’ food security. Agronomy, 14 (7), 1–22. https://doi.org/10.3390/agronomy14071540 African Union. (2014). Malabo Declaration on Accelerated Agricultural Growth and Transformation for Shared Prosperity and Improved Livelihoods. African Union Commission. https://au.int/en/documents/20210204/malabo-declaration-progress-report Albayati, H., Alistarbadi, N., & Rho, J. J. (2023). Assessing engagement decisions in NFT metaverse based on the Theory of Planned Behavior (TPB). Telematics and Informatics Reports, 10 , 100045. https://doi.org/10.1016/j.teler.2023.100045 Ayanwale, M. A., Molefi, R. R., & Matsie, N. (2023). Modelling secondary school students’ attitudes toward TVET subjects using social cognitive and planned behavior theories. Social Sciences and Humanities Open, 8 (1), 100478. https://doi.org/10.1016/j.ssaho.2023.100478 Aziz, M. A., Ayob, N. H., Ayob, N. A., Ahmad, Y., & Abdulsomad, K. (2024). Factors influencing farmer adoption of climate-smart agriculture technologies: Evidence from Malaysia. Human Technology, 20 (1), 70–92. https://doi.org/10.14254/1795-6889.2024.20-1.4 Bhatti, M. A., Godfrey, S. S., Divon, S. A., Aamodt, J. T., Øystese, S., Wynn, P. C., Eik, L. O., & Fjeld-Solberg, Ø. (2022). Micro-investment by Tanzanian smallholders in drip irrigation kits for vegetable production to improve livelihoods: Lessons learned and a way forward. Agriculture, 12 (10), 1732. https://doi.org/10.3390/agriculture12101732 Bongole, A. (2023). Adoption of multiple climate-smart agricultural practices in Mbeya and Songwe regions in Tanzania. Journal of African Economic Perspectives, 1 (1), 41–60. CGIAR. (2021). Climate-smart agriculture policy implementation. https://www.cgiar.org/impact/ Ebrahimi, S. S., Lashgharara, F., Mirdamadi, S. M., & Najafabadi, M. O. (2023). Factors influencing climate change adaptation practices and their impacts on food security dimensions in horticultural crops evaluated using PLS-SEM analysis. Bulgarian Journal of Agricultural Science, 29 (5), 978–993. Fawehinmi, O., Yusliza, M. Y., Tanveer, M. I., & Abdullahi, M. S. (2024). Influence of green human resource management on employee green behavior: The sequential mediating effect of perceived behavioral control and attitude toward corporate environmental policy. Corporate Social Responsibility and Environmental Management, 31 (3), 2514–2536. https://doi.org/10.1002/csr.2707 Food and Agriculture Organization (FAO). (2021). Financing climate-smart agriculture: A guide to investment models and business cases. https://www.fao.org/publications/card/en/c/CB8091EN/ International Fund for Agricultural Development (IFAD). (2019). Tanzania climate-smart agriculture program. https://www.ifad.org/en/web/operations/-/project/tanzania-climate-smart-agriculture Kebenei, P., Mutunga, W., & Wambua, J. (2023). The role of microfinance in climate adaptation among smallholder farmers. African Journal of Rural Development, 9 (1), 78–95. Lindawati, A. S. L., Handoko, B. L., & Mustapha, M. (2023). Planned behavior and social cognitive in predicting e-business intention to adopt eco-design. ACM International Conference Proceeding Series, 73 (1), 79. https://doi.org/10.1145/3584816.3584827 Mang’ana, K. M., Hokororo, S. J., & Ndyetabula, D. W. (2024). An Investigation of the Extent of Implementation of the Financial Management Practices of Agri-SMEs in developing countries: Evidence from Tanzania. Sustainable Technology and Entrepreneurship, 3(1), 100049. https://doi.org/https://doi.org/10.1016/j.stae.2023.100049 Ma, W., & Rahut, D. B. (2024). Climate-smart agriculture: Adoption, impacts, and implications for sustainable development. Mitigation and Adaptation Strategies for Global Change, 29 (5), 10139. https://doi.org/10.1007/s11027-024-10139-z Meshesha, A. T., Birhanu, B. S., & Ayele, M. B. (2022). Effects of perceptions on adoption of climate-smart agriculture innovations: Empirical evidence from the upper Blue Nile Highlands of Ethiopia. International Journal of Climate Change Strategies and Management, 14 (3), 293–311. https://doi.org/10.1108/IJCCSM-04-2021-0035 Roman Aschinger, S., Boillat, S., & Chinwe Ifejika Speranza, C. (2023). Smallholder livelihood resilience to climate variability in South-Eastern Kenya, 2012–2015. Frontiers in Sustainable Food Systems, 7 , 15–32. https://doi.org/10.3389/fsufs.2023.00099 Singh, R., Mir, M. A., & Nazki, A. A. (2024). Evaluation of tourist behavior towards traditional food consumption: Validation of extended Theory of Planned Behaviour. Cogent Social Sciences, 10 (1), 2298893. https://doi.org/10.1080/23311886.2023.2298893 United Nations Environment Programme (UNEP). (2020). Climate adaptation and resilience strategies in agriculture. https://www.unep.org/resources/publication United Republic of Tanzania (URT). (2012). National Climate Change Strategy. https://www.tanzania.go.tz/ United Republic of Tanzania (URT). (2013). National Agriculture Policy. Ministry of Agriculture. https://www.kilimo.go.tz/uploads/NATIONAL_AGRICULTURAL_POLICY.pdf United Republic of Tanzania (URT). (2017). Climate-Smart Agriculture Guidelines. https://www.fao.org/3/i8757en/I8757EN.pdf United States Agency for International Development (USAID). (2021). Agriculture and market linkages in Tanzania. https://www.usaid.gov/tanzania/agriculture World Bank. (2020). Digital platforms for agricultural extension services in Africa. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/JIM-03-2022-0122 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6024538","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415777862,"identity":"dc03a46d-40cd-49eb-8c70-d15fa8d0f34d","order_by":0,"name":"STEPHEN BISHIBURA ERICK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYJCCAwwHbHgYGBJI05IG1yJBrKbDDMRrkW/vfXjgx5nzMubtuQcYftQw1Mk3ENBicOa4wcGeG7d5ZM68S2DsOcYgwUhQi0Qaw2GGD7d5JCRyDBh4GxgkmAk6bP4zkJZzYC2Mf4Fa2Ah65gYbUMuNA2AtzCBbeAjpMDiTxnCw50wyjwTPu4TDMsckJGcQdFj7MeYPP47Z2Uuw5x58+KbGhp9giCEBHmCcEh2TMC2jYBSMglEwCrACAL0fO5zDBnojAAAAAElFTkSuQmCC","orcid":"","institution":"Sokoine University of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"STEPHEN","middleName":"BISHIBURA","lastName":"ERICK","suffix":""},{"id":415777863,"identity":"3927868d-607b-4aba-a739-6c89b0003646","order_by":1,"name":"Jonathan Stephen Mbwambo","email":"","orcid":"","institution":"Sokoine University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"Stephen","lastName":"Mbwambo","suffix":""},{"id":415777864,"identity":"95f7fb81-b27d-4a04-b3ce-342e3ffbb7d2","order_by":2,"name":"Raymond Salanga","email":"","orcid":"","institution":"Sokoine University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Raymond","middleName":"","lastName":"Salanga","suffix":""}],"badges":[],"createdAt":"2025-02-13 15:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6024538/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6024538/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76492978,"identity":"dcb80a72-c84c-4dbb-9334-e2c9969d68d3","added_by":"auto","created_at":"2025-02-17 17:21:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177701,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework\u003c/p\u003e\n\u003cp\u003eThe hypothesized relationships are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH01\u003c/strong\u003e Positive attitudes (ATT) toward CSA practices does not increase farmers perceived: usefulness (PU) of CSA practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH02:\u003c/strong\u003e \u0026nbsp;Positive subjective norms (SN) do not influence farmers’ perceived usefulness (PU) of CSA practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH03:\u003c/strong\u003e \u0026nbsp;Higher perceived behavioral control (PBC) does not positively influence farmers’ perceived usefulness (PU) of CSA practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH04:\u003c/strong\u003e \u0026nbsp;The perceived usefulness (PU) of CSA practices do not positively influences their adoption of CSA practices\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6024538/v1/7ecece135ad857789346fa65.png"},{"id":76493486,"identity":"681aecf9-0f0a-4793-a7b3-70db40a84416","added_by":"auto","created_at":"2025-02-17 17:29:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307495,"visible":true,"origin":"","legend":"\u003cp\u003epresents maps illustrating the administrative divisions of Dodoma and Singida regions, as well as the specific wards where data were collected.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6024538/v1/d7213500a5f77a2d83ea42fc.png"},{"id":76492977,"identity":"fbe4288c-35a4-49c2-8a5b-ce2a18b74acc","added_by":"auto","created_at":"2025-02-17 17:21:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61377,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model with Path Coefficients and R-squared Values\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6024538/v1/ebb81f740ec3be4040df62b8.png"},{"id":76512856,"identity":"4c00c510-feee-4011-998b-4f509b20114f","added_by":"auto","created_at":"2025-02-18 02:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1608184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6024538/v1/75dde06b-370e-4016-8c67-e33ed141a495.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioral Determinants of Climate-Smart Agriculture Adoption among Smallholder Leafy Vegetable Agripreneurs in Semi-Arid Central Tanzania","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change significantly affects agricultural productivity, particularly in semi-arid regions where smallholder agripreneurs face erratic weather patterns and extreme climatic events (International Fund for Agricultural Development [IFAD], 2019; Lindawati, Handoko, \u0026amp; Mustapha, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; United Nations Environment Programme [UNEP], 2020).Prolonged dry spells, irregular rainfall, and rising temperatures disrupt traditional farming systems, necessitating the urgent adoption of adaptive strategies to sustain productivity and ensure food security (Kebenei, Mucheru-Muna, \u0026amp; Muriu-Nganga, 2023). Climate-smart agriculture (CSA) offers a viable solution by enhancing productivity, strengthening resilience to climate variability, and reducing greenhouse gas emissions (Ng'ang'a, Miller, \u0026amp; Girvetz, 2021; Ogada, Radeny, Recha, \u0026amp; Dawit, 2021). However, the effectiveness of CSA practices depends on selecting crops suited to specific agroecological zones (Ngetich, Mairura, Musafiri, Kiboi, \u0026amp; Shisanya, 2022; Yusuph, Nzunda, Mourice, \u0026amp; Dalgaard, 2023).\u003c/p\u003e \u003cp\u003eLeafy vegetables are well-suited to cultivation in semi-arid regions like Central Tanzania due to their adaptability, short production cycles, high market demand, and multiple harvest potential, which offer advantages over staple crops (Imathiu, 2021; Sarker \u0026amp; Oba, 2020). Integrating these crops into farming systems can enhance household incomes, diversify production, and improve food security (Adebiyi, Olabisi, Liu, \u0026amp; Jordan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quansah \u0026amp; Chen, 2021). However, successful cultivation in these environments requires adopting CSA practices that mitigate climate-related stresses.\u003c/p\u003e \u003cp\u003eThis study examines a range of leafy vegetables cultivated in Central Tanzania, including both common and indigenous varieties such as amaranth (\u003cem\u003eAmaranthus\u003c/em\u003e species), coriander (\u003cem\u003eCoriandrum sativum\u003c/em\u003e), sweet potato leaves (\u003cem\u003eIpomoea batatas\u003c/em\u003e), pumpkin leaves (\u003cem\u003eCucurbita\u003c/em\u003e species), Chinese cabbage (\u003cem\u003eBrassica rapa\u003c/em\u003e), kale (\u003cem\u003eBrassica oleracea\u003c/em\u003e), spinach (\u003cem\u003eSpinacia oleracea\u003c/em\u003e), collard greens (\u003cem\u003eBrassica oleracea\u003c/em\u003e), and local varieties like \u0026lsquo;saladi\u0026rsquo; and \u0026lsquo;mahanjo.\u0026rsquo; These crops contribute significantly to food security and climate adaptation. To enhance resilience and sustainability, smallholder agripreneurs adopt CSA practices, including crop diversification, rotation, soil mulching, improved seeds, integrated soil fertility management, and agroforestry.\u003c/p\u003e \u003cp\u003eWhile CSA practices aim to sustainably increase productivity, resilience, and reduce emissions (Aziz, Ayob, Ayob, Ahmad, \u0026amp; Abdulsomad, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Roman Aschinger, Boillat, \u0026amp; Chinwe Ifejika Speranza, 2023), their adoption is influenced by resource availability, institutional support, market conditions, and behavioral factors such as income levels, education, and attitudes (Bongole, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang, 2024). Behavioral determinants, including attitudes toward CSA, social norms within farming communities, and perceived behavioral control, play a crucial role in adoption decisions (Fawehinmi, Yusliza, Tanveer, \u0026amp; Abdullahi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma \u0026amp; Rahut, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, research exploring these factors among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania remains limited.\u003c/p\u003e \u003cp\u003eAttitudes, or positive perceptions of CSA\u0026rsquo;s benefits, strongly influence adoption (Maziriri, Nyagadza, Chuchu, \u0026amp; Mazuruse, 2023; Zheng, Kumar, Kunasekaran, \u0026amp; Valeri, 2024). Social norms shape adoption decisions through peer influence (Ayanwale, Molefi, \u0026amp; Matsie, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nugraha, Wahib Muhaimin, Maulidah, Widya Putri, \u0026amp; Maulidah, 2024; Singh, Mir, \u0026amp; Nazki, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while perceived behavioral control\u0026mdash;agripreneurs\u0026rsquo; confidence in implementing CSA practices\u0026mdash;affects their willingness to adopt (Albayati, Alistarbadi, \u0026amp; Rho, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fawehinmi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite the relevance of these factors, their role in CSA adoption among smallholder leafy vegetable agripreneurs in semi-arid regions remains underexplored, necessitating further investigation to inform targeted interventions.\u003c/p\u003e \u003cp\u003eThis study examines the behavioral factors influencing CSA adoption among smallholder leafy vegetable agripreneurs in Central Tanzania, focusing on attitudes, social norms, and perceived behavioral control. Guided by Ajzen\u0026rsquo;s Theory of Planned Behavior (TPB), this research provides insights for policymakers, agricultural extension services, and other stakeholders to promote sustainable farming methods in resource-limited settings.\u003c/p\u003e \u003cp\u003eThis paper is organized as follows: Section 1 introduces the research topic, outlining climate change challenges, the role of CSA, and key behavioral factors. It also presents the theoretical framework, discussing Ajzen\u0026rsquo;s Theory of Planned Behavior. Section 2 details the methodology, covering the study area, research design, and data analysis. Section 3 presents the results, highlighting behavioral factors influencing CSA adoption. Section 4 discusses these findings in relation to existing research, emphasizing their contributions. Section 5 concludes with practical recommendations for policymakers and stakeholders, stressing the importance of integrating behavioral considerations into CSA interventions.\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis study is grounded in Ajzen\u0026rsquo;s Theory of Planned Behavior (TPB), a widely recognized model for predicting human behavior. TPB posits that an individual's intention to perform a behavior is the strongest predictor of actual behavior, shaped by three key factors: attitudes, subjective norms, and perceived behavioral control (Ajzen, 1991).\u003c/p\u003e \u003cp\u003eAttitudes reflect an individual\u0026rsquo;s evaluation of a behavior\u0026rsquo;s desirability. In this study, they represent agripreneurs\u0026rsquo; perceptions of the benefits of climate-smart agriculture (CSA) adoption, particularly regarding productivity and resilience. Subjective norms capture social pressures from peers, community members, and experts, influencing adoption decisions. Perceived behavioral control (PBC) denotes agripreneurs\u0026rsquo; confidence in their ability to implement CSA practices, shaped by access to resources, technical knowledge, and institutional support.\u003c/p\u003e \u003cp\u003eWhile TPB underscores behavioral intention as a key determinant, external constraints, such as resource limitations and climate uncertainties, may disrupt this relationship. To address these complexities, this study integrates perceived usefulness as a mediating factor, refining the model\u0026rsquo;s applicability to CSA adoption. This addition enhances understanding of how behavioral determinants translate into actual adoption among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania.\u003c/p\u003e \u003cp\u003eThe TPB model applied in this study is expressed as:\u003c/p\u003e \u003cp\u003eINT\u0026thinsp;=\u0026thinsp;β₁ ATT\u0026thinsp;+\u0026thinsp;β₂ SN\u0026thinsp;+\u0026thinsp;β₃ PBC\u0026thinsp;+\u0026thinsp;ε,\u003c/p\u003e \u003cp\u003ewhere the intention (INT) to adopt CSA practices is a function of attitudes (ATT), subjective norms (SN), and perceived behavioral control (PBC). The coefficients (β₁, β₂, β₃) represent the relative importance of each factor, while the error term (ε) accounts for other unexplained influences. Although TPB has been widely applied in agricultural research, its use in understanding CSA adoption behavior among leafy vegetable agripreneurs in semi-arid regions has been limited. This study aims to fill this gap by investigating how these behavioral factors influence the adoption of CSA practices in Central Tanzania.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual Framework\u003c/h2\u003e \u003cp\u003eBuilding on the theoretical framework, this study\u0026rsquo;s conceptual framework incorporates perceived usefulness as a mediating factor, providing a structured approach to understanding the behavioral determinants of climate-smart agriculture (CSA) adoption among smallholder leafy vegetable agripreneurs in semi-arid regions. By integrating the psychological principles of the Theory of Planned Behavior (TPB) with contextual factors relevant to resource-limited settings, the framework offers a comprehensive model for analyzing decision-making processes.\u003c/p\u003e \u003cp\u003eThe framework posits that attitudes toward CSA practices positively influence perceived usefulness, suggesting that when farmers recognize benefits such as improved productivity and resilience, they are more likely to view CSA as valuable and adopt it. Subjective norms, shaped by social pressures from peers, family members, and agricultural extension officers, also influence perceived usefulness by reinforcing the perceived advantages of CSA adoption. Perceived behavioral control, reflecting agripreneurs' confidence in accessing resources and implementing CSA, directly affects both perceived usefulness and adoption behavior. Perceived usefulness serves as the key link, translating attitudes, social norms, and control perceptions into concrete adoption decisions by highlighting CSA\u0026rsquo;s practical benefits, including enhanced productivity, financial viability, and climate resilience. These relationships are illustrated in the conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), mapping the pathways leading to CSA adoption.\u003c/p\u003e \u003cp\u003eBy integrating perceived usefulness as a mediating factor, this framework bridges the gap between behavioral intention and actual adoption, addressing both psychological drivers and practical constraints. It extends the application of TPB in agricultural research by capturing the unique challenges faced by agripreneurs in resource-scarce environments. This model, tailored to CSA adoption in Central Tanzania, provides valuable insights for promoting sustainable farming practices and guiding policy interventions that support smallholder agripreneurs in adapting to climate change.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the semi-arid regions of Central Tanzania, focusing on Ihumwa and Iyumbu Wards in Dodoma City and Uhamaka and Unyambwa Wards in Singida Municipality. These areas are characterized by unpredictable rainfall patterns, prolonged dry spells, and rising temperatures, making them particularly vulnerable to the adverse effects of climate change (United Republic of Tanzania [URT], 2021; Mwamfupe, 2015). The predominant reliance on rain-fed agriculture in these regions underscores the urgent need for adaptive strategies to enhance agricultural resilience.\u003c/p\u003e \u003cp\u003eDodoma City and Singida Municipality were purposively selected due to their significant contribution to leafy vegetable production and high exposure to climate variability, making them ideal for investigating the behavioral determinants of climate-smart agriculture (CSA) adoption (Ekka \u0026amp; Mjawa, 2020). To provide geographical context, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents maps illustrating the administrative divisions of Dodoma and Singida regions, as well as the specific wards where data were collected. These maps, developed using data from the National Bureau of Statistics (NBS) and OpenStreetMap Contributors, highlight the spatial distribution of the selected wards and their significance within the semi-arid zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Design and Sampling\u003c/h3\u003e\n\u003cp\u003eThis study employed a cross-sectional survey design to collect data at a single point in time, providing a snapshot of the relationships between behavioral factors and the adoption of climate-smart agriculture (CSA) practices. The design was informed by Ajzen's (1991) Theory of Planned Behavior, which was expanded to include perceived usefulness as a mediating factor. This framework facilitated a comprehensive analysis of how attitudes, subjective norms, and perceived behavioral control influence CSA adoption through the mediating role of perceived usefulness.\u003c/p\u003e \u003cp\u003eTo ensure representativeness, a multi-stage sampling technique was employed. Four wards were purposively selected due to their significance in leafy vegetable production. Within each ward, villages were stratified by production scale and crop variety, and respondents were randomly selected from each stratum to ensure a diverse and balanced sample. The final sample size of 385 respondents was determined using Cochran's formula, based on a 95% confidence level and a 5% margin of error. Among these, 193 participants were from Dodoma City, and 192 were from Singida Municipality, ensuring proportional representation across the selected study sites.\u003c/p\u003e\n\u003ch3\u003eData Collection Instrument and Pre-Testing\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured questionnaire designed to assess the behavioral factors influencing CSA adoption. The questionnaire consisted of sections covering attitudes toward CSA practices, subjective norms, perceived behavioral control, perceived usefulness, and actual CSA adoption behavior. Attitudes were assessed through items on perceived benefits, such as increased productivity and resilience. Subjective norms were examined by evaluating peer, family, and extension officer influences on CSA adoption decisions. Perceived behavioral control was measured based on access to financial resources, technical knowledge, and institutional support. Perceived usefulness focused on practical benefits, including higher yields and economic returns, while CSA adoption behavior was assessed by the frequency of practices such as crop rotation, mulching, and the use of improved seeds. Responses were rated on a five-point Likert scale, ranging from strongly disagree to strongly agree.\u003c/p\u003e \u003cp\u003eThe questionnaire was pre-tested with 30 leafy vegetable agripreneurs in Ikungi District, a location with similar agroecological conditions. Feedback from the pre-test was used to refine wording, structure, and translation to enhance clarity and cultural relevance. Reliability was confirmed through Cronbach\u0026rsquo;s alpha values exceeding 0.7 for all constructs, indicating strong internal consistency. Content validity was ensured through reviews by agricultural extension officers and CSA experts familiar with the Tanzanian agricultural context.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eThe study received ethical approval from the relevant institutional review board. Informed consent was obtained from all participants, who were assured of confidentiality and anonymity. Participation was voluntary, and respondents had the right to withdraw at any time without consequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe data analysis was conducted using Structural Equation Modeling (SEM) with Smart PLS 4 to examine the relationships between the latent variables. A structured two-step approach was followed: first, the measurement model was assessed to ensure reliability and validity, and second, the structural model was evaluated to test the hypothesized relationships.\u003c/p\u003e \u003cp\u003eTo assess internal consistency, Cronbach's alpha and composite reliability were used, both exceeding the widely accepted threshold of 0.7. Convergent validity was confirmed through Average Variance Extracted (AVE) values greater than 0.5, while discriminant validity was established using the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio. These measures ensured that all constructs were both reliable and distinct.\u003c/p\u003e \u003cp\u003eFor the structural model, bootstrapping with 5,000 resamples was performed to compute path coefficients, t-values, and p-values, providing statistical significance for the proposed relationships. The model\u0026rsquo;s explanatory power was evaluated using R-squared values, which determined the proportion of variance in the dependent variables explained by the independent ones. Additionally, model fit was assessed using the Standardized Root Mean Square Residual (SRMR), which confirmed a good fit, along with other indices such as d_ULS and the Normed Fit Index (NFI). During the refinement process, indicators were carefully reviewed. Items with low factor loadings (below 0.7) or high cross-loadings were systematically removed to improve the model\u0026rsquo;s overall quality and performance. This rigorous approach ensured a robust and reliable analysis, enhancing the validity of the study\u0026rsquo;s findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Model Assessment\u003c/h2\u003e \u003cp\u003eThe measurement model was evaluated to ensure the reliability and validity of the constructs used in the study. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, factor loadings for the retained indicators ranged from 0.705 to 0.918, indicating strong correlations between the observed variables and their respective constructs. Reliability was confirmed using Cronbach's alpha and composite reliability, both of which exceeded the recommended threshold of 0.7 for all constructs as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Convergent validity was established through Average Variance Extracted (AVE) values exceeding 0.5, demonstrating that a significant portion of the variance in the observed variables was accounted for by their respective constructs.\u003c/p\u003e \u003cp\u003eDiscriminant validity was assessed using the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the square root of the AVE for each construct exceeded its correlations with other constructs, confirming that the constructs were sufficiently distinct. Additionally, HTMT ratios were below the strict threshold of 0.85, further supporting discriminant validity. These results indicate that the measurement model exhibited strong psychometric properties, ensuring reliability and validity for the subsequent structural analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor Loadings of Retained Indicators for the Measurement Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude CSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSA Adoption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerceived Behavior Control (PBC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerceived Useful\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSocial Norms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD_8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATT_9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEU_1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEU_2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEU_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEU_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEU_5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePU_1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePU_2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePU_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePU_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSN_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSN_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSN_5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSN_6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and Validity of Constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude CSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Norms (SN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterotrait-Monotrait (HTMT) ratio\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude CSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSA Adoption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerceived Behavior\u003c/p\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(PBC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerceived Useful\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSocial Norms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude CSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSA Adoption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Behavior Control (PBC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Useful\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Norms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRevised Structural Model Assessment (for Results section)\u003c/h2\u003e \u003cp\u003eThe structural model was evaluated to test the hypothesized relationships between the constructs. Bootstrapping with 5,000 resamples was employed to estimate path coefficients, t-values, and p-values, determining the statistical significance of the proposed relationships. The model\u0026rsquo;s explanatory power was assessed using R-squared (R\u0026sup2;) values. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, perceived usefulness (R\u0026sup2; = 0.612) was influenced by attitudes, subjective norms, and perceived behavioral control, while CSA adoption behavior (R\u0026sup2; = 0.267) was shaped by perceived usefulness and other mediators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel Fit and Explanatory Power\u003c/h2\u003e \u003cp\u003eThe explanatory power of the structural model was assessed using R-square (R\u0026sup2;) values, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The model explained 26.7% (R\u0026sup2; = 0.267) of the variance in CSA adoption behavior, while perceived usefulness accounted for 61.2% (R\u0026sup2; = 0.612) of the variance. The adjusted R-square values (0.266 for CSA adoption and 0.609 for perceived usefulness) were slightly lower than the unadjusted values, indicating a stable model fit. The model fit indices, including the SRMR value of 0.078, suggest an acceptable fit between the observed data and the proposed model structure. These results suggest that the extended Theory of Planned Behavior provides a statistically sound explanation of the behavioral determinants influencing CSA adoption, capturing both direct and mediated effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit and Explanatory Power\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-square adjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Useful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis Testing\u003c/h2\u003e \u003cp\u003eThe hypothesized relationships were tested, and the results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All direct and indirect effects were statistically significant at the 95% confidence level, supporting the proposed conceptual framework. Attitudes (β\u0026thinsp;=\u0026thinsp;0.528, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) emerged as the strongest predictor of perceived usefulness, followed by subjective norms (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and perceived behavioral control (β\u0026thinsp;=\u0026thinsp;0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results indicate that farmers\u0026rsquo; evaluations of CSA practices, social influences, and their perceived control over adoption significantly contribute to their perceptions of CSA\u0026rsquo;s usefulness.\u003c/p\u003e \u003cp\u003ePerceived usefulness had a significant direct effect on CSA adoption behavior (β\u0026thinsp;=\u0026thinsp;0.580, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the analysis revealed significant indirect effects, demonstrating the mediating role of perceived usefulness in the relationship between attitudes, subjective norms, perceived behavioral control, and CSA adoption behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis Testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1: Attitudes \u0026rarr; Perceived Usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATT \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2: Subjective Norms \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3: PBC \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBC \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4: PU \u0026rarr; CSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect: ATT \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATT \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect: SN \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect: PBC \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBC \u0026rarr; PU \u0026rarr; Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis\u003c/h2\u003e \u003cp\u003eThe mediation effects of perceived usefulness in the relationship between attitudes, subjective norms, and perceived behavioral control on CSA adoption were examined. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all indirect effects were statistically significant. The indirect effect of attitudes on CSA adoption through perceived usefulness was 0.273 (t\u0026thinsp;=\u0026thinsp;5.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a strong mediation effect. Similarly, subjective norms influenced CSA adoption through perceived usefulness with an indirect effect of 0.190 (t\u0026thinsp;=\u0026thinsp;4.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The indirect effect of perceived behavioral control through perceived usefulness was also significant at 0.135 (t\u0026thinsp;=\u0026thinsp;3.21, p\u0026thinsp;=\u0026thinsp;0.002). These results confirm the mediating role of perceived usefulness in explaining the influence of behavioral determinants on CSA adoption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect Effect Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT \u0026rarr; PU \u0026rarr; CSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN \u0026rarr; PU \u0026rarr; CSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC \u0026rarr; PU \u0026rarr; CSA Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral Determinants of CSA Adoption\u003c/h2\u003e \u003cp\u003eThis study examined the behavioral factors influencing the adoption of climate-smart agriculture (CSA) practices among smallholder leafy vegetable agripreneurs in semi-arid regions of Central Tanzania. Using an extended version of Ajzen\u0026rsquo;s Theory of Planned Behavior (TPB) (Ajzen, 1991), it analyzed how attitudes, subjective norms, perceived behavioral control, and perceived usefulness interact in shaping adoption decisions.\u003c/p\u003e \u003cp\u003eThe findings indicate that attitudes were the strongest predictor of both perceived usefulness and CSA adoption behavior, underscoring the role of positive perceptions in driving the uptake of sustainable practices. This aligns with previous studies highlighting the importance of attitudinal factors in promoting environmentally friendly behaviors (Ma \u0026amp; Rahut, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maziriri et al., 2023). Farmers who associate CSA practices with tangible benefits such as higher yields, improved soil health, and resilience to climate variability are more likely to adopt them. Strengthening extension services, farmer field schools, and demonstration plots can reinforce positive attitudes by demonstrating CSA's practical advantages (Bhatti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngetich et al., 2022).\u003c/p\u003e \u003cp\u003eSubjective norms also emerged as a significant determinant of perceived usefulness and, indirectly, adoption behavior, emphasizing the role of social influence in decision-making. In rural farming communities, agricultural choices are often shaped by family members, peers, and community leaders (Ayanwale et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Strengthening social learning networks, farmer cooperatives, and community-based knowledge-sharing platforms can facilitate peer-to-peer learning and encourage CSA adoption (Adebiyi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kebenei et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePerceived behavioral control, reflecting agripreneurs\u0026rsquo; confidence in their ability to implement CSA practices, was influenced by access to resources, technical knowledge, and institutional support. This finding aligns with prior research identifying resource constraints as a major barrier to sustainable agricultural practices (Fawehinmi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kebenei et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Enhancing access to input subsidies, capacity-building programs, and credit facilities can improve farmers\u0026rsquo; confidence in adopting CSA (Bhatti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngetich et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMediating Role of Perceived Usefulness\u003c/h2\u003e \u003cp\u003ePerceived usefulness significantly mediated the relationship between behavioral determinants and CSA adoption. The model showed that 61.2% of the variance in perceived usefulness (R\u0026sup2; = 0.612, adjusted R\u0026sup2; = 0.609) was explained by attitudes, subjective norms, and perceived behavioral control. In turn, CSA adoption behavior was influenced by perceived usefulness and other predictors, accounting for 26.7% of the variance (R\u0026sup2; = 0.267, adjusted R\u0026sup2; = 0.266). These findings confirm that perceived usefulness serves as a crucial link between behavioral determinants and CSA adoption, reinforcing its mediating role within the extended TPB framework (Fawehinmi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma \u0026amp; Rahut, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mang\u0026rsquo;ana, Hokororo, \u0026amp; Ndyetabula, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strong explanatory power of perceived usefulness suggests that agripreneurs who perceive CSA as beneficial\u0026mdash;resulting in higher productivity, economic gains, and resilience\u0026mdash;are more likely to adopt these practices (Adebiyi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kebenei et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This supports previous studies indicating that farmers' adoption decisions are primarily driven by their perception of tangible benefits from new agricultural technologies (Maziriri et al., 2023; Singh et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To enhance CSA adoption, interventions should prioritize awareness campaigns, training programs, and demonstrations that effectively communicate the practical advantages of CSA, ensuring that farmers view these practices as both feasible and beneficial (Bhatti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngetich et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Contributions\u003c/h2\u003e \u003cp\u003eThis study contributes to the theoretical advancement of agricultural innovation adoption by extending the Theory of Planned Behavior (TPB) to include perceived usefulness as a mediating factor. This extension provides a more comprehensive model for understanding the complex interactions between behavioral determinants and CSA adoption. While previous studies have examined the direct relationships between attitudes, subjective norms, perceived behavioral control, and adoption behavior, they have often overlooked the mediating role of perceived usefulness in shaping adoption decisions (Fawehinmi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma \u0026amp; Rahut, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, this study enriches the existing literature on the psychosocial dimensions of agricultural innovation adoption, particularly within smallholder farming systems in sub-Saharan Africa (Adebiyi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngetich et al., 2022). By focusing on leafy vegetable agripreneurs in semi-arid Tanzania, the findings provide context-specific insights that can inform targeted interventions to promote CSA adoption in similar agricultural settings. These insights contribute to a deeper understanding of how behavioral factors interact within resource-constrained environments, offering practical implications for policymakers and development practitioners aiming to enhance the adoption of sustainable agricultural practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Implications\u003c/h2\u003e \u003cp\u003eThis study underscores the need for targeted policies to enhance climate-smart agriculture (CSA) adoption among smallholder leafy vegetable farmers in semi-arid central Tanzania. Aligning CSA interventions with Tanzania\u0026rsquo;s National Agriculture Policy (NAP, 2013), Climate-Smart Agriculture Guidelines (URT, 2017), and the Horticulture Development Strategy (THDS, 2012) is critical for sustainable agricultural transformation. Strengthening public-private partnerships (PPPs) in CSA financing, infrastructure, and extension services will create a more enabling policy environment for CSA adoption (FAO, 2021).\u003c/p\u003e \u003cp\u003eEnhancing agricultural extension services is fundamental to improving CSA knowledge and adoption rates. The NAP (2013) and THDS (2012) emphasize the role of extension services in boosting agricultural productivity and resilience. Expanding CSA-focused training through farmer field schools, digital platforms, and participatory programs would increase knowledge dissemination and practical adoption. Integrating CSA into Tanzania\u0026rsquo;s E-Agriculture Strategy (URT, 2016) would further expand outreach, particularly for smallholder farmers in remote areas (World Bank, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreasing financial accessibility is essential, as high input costs and limited capital prevent widespread CSA adoption. The Tanzania Agriculture and Food Security Investment Plan (TAFSIP, 2011) emphasizes the role of financial inclusion in transforming agriculture. Policies should scale up targeted subsidies, provide low-interest loans, and expand climate-risk insurance through TADB and microfinance institutions. Encouraging blended finance models and private-sector investments can further enhance access to CSA technologies, credit, and resilient farming inputs (IFAD, 2019; FAO, 2021).\u003c/p\u003e \u003cp\u003eLeveraging farmer cooperatives and networks, investing in climate-resilient infrastructure, and promoting agribusiness collaborations will further strengthen CSA adoption. Aligning national CSA strategies with regional frameworks such as the Malabo Declaration (2014) can enhance funding opportunities while contributing to food security, climate resilience, and smallholder economic empowerment (African Union, 2021). The integration of these measures into national and regional policies, stakeholders can create an enabling environment that supports widespread CSA adoption, contributing to food security (SDG 2), climate resilience (SDG 13), and smallholder economic empowerment in Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the Study\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights into the behavioral determinants of CSA adoption, several limitations must be acknowledged. First, its cross-sectional design captures behavioral factors at a single point in time, limiting causal interpretations. Second, the focus on leafy vegetable agripreneurs in semi-arid Tanzania may constrain the generalizability of findings to other crops and farming systems. Third, reliance on self-reported data introduces potential social desirability bias, possibly inflating CSA adoption rates. These limitations underscore the need for further research to deepen understanding of CSA adoption dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides empirical evidence on the behavioral factors driving CSA adoption among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania. The findings confirm that attitudes, subjective norms, and perceived behavioral control significantly influence adoption, with perceived usefulness serving as a key mediator. These results reinforce the need for targeted interventions to promote CSA adoption through awareness campaigns, capacity-building initiatives, and improved access to financial and institutional support. Social networks and farmer cooperatives should be leveraged to enhance knowledge dissemination and peer learning. Additionally, policies aligning with Tanzania\u0026rsquo;s National Agriculture Policy and Climate-Smart Agriculture Guidelines should focus on strengthening public-private partnerships, improving rural infrastructure, and expanding digital extension services. While the study's cross-sectional nature limits causal inference, it offers a foundational understanding of the psychosocial dynamics affecting CSA adoption. Future research should employ longitudinal methods and explore economic and market-driven factors influencing CSA uptake. By addressing behavioral barriers and enhancing perceived usefulness, stakeholders can foster sustainable agricultural transitions that enhance food security and resilience in climate-vulnerable regions.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations for Future Research\u003c/h2\u003e \u003cp\u003eTo address these limitations, future studies should adopt longitudinal designs to track changes in behavioral determinants and CSA adoption over time. Comparative analyses across diverse agroecological zones and farming systems are essential to assess contextual variations in adoption drivers. Additionally, integrating objective farm-level assessments, remote sensing technologies, and observational data would enhance measurement accuracy. Expanding research to examine economic and market-driven factors, such as input costs, market access, and supply chain efficiency, could provide a more comprehensive understanding of the barriers and enablers of CSA adoption.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"S.B.E\" collected data, analysed data and wrote the main manuscript.\"J.S.M\" Prepared data collection tools and reviewed the first draft.\"R.S\" Prepared data analysis software, analysed data, and reviewed the final draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebiyi, J. A., Olabisi, L. S., Liu, L., \u0026amp; Jordan, D. (2020). Water\u0026ndash;food\u0026ndash;energy\u0026ndash;climate nexus and technology productivity: A Nigerian case study of organic leafy vegetable production. \u003cem\u003eEnvironment, Development and Sustainability, 23\u003c/em\u003e(4), 6128\u0026ndash;6147. https://doi.org/10.1007/s10668-020-00865-0\u003c/li\u003e\n\u003cli\u003eAffoh, R., Zheng, H., Zhang, X., Wang, X., Dangui, K., \u0026amp; Zhang, L. (2024). Climate-smart agriculture as an adaptation measure to climate change in Togo: Determinants of choices and its impact on rural households\u0026rsquo; food security. \u003cem\u003eAgronomy, 14\u003c/em\u003e(7), 1\u0026ndash;22. https://doi.org/10.3390/agronomy14071540\u003c/li\u003e\n\u003cli\u003eAfrican Union. (2014). \u003cem\u003eMalabo Declaration on Accelerated Agricultural Growth and Transformation for Shared Prosperity and Improved Livelihoods.\u003c/em\u003e African Union Commission. https://au.int/en/documents/20210204/malabo-declaration-progress-report\u003c/li\u003e\n\u003cli\u003eAlbayati, H., Alistarbadi, N., \u0026amp; Rho, J. J. (2023). Assessing engagement decisions in NFT metaverse based on the Theory of Planned Behavior (TPB). \u003cem\u003eTelematics and Informatics Reports, 10\u003c/em\u003e, 100045. https://doi.org/10.1016/j.teler.2023.100045\u003c/li\u003e\n\u003cli\u003eAyanwale, M. A., Molefi, R. R., \u0026amp; Matsie, N. (2023). Modelling secondary school students\u0026rsquo; attitudes toward TVET subjects using social cognitive and planned behavior theories. \u003cem\u003eSocial Sciences and Humanities Open, 8\u003c/em\u003e(1), 100478. https://doi.org/10.1016/j.ssaho.2023.100478\u003c/li\u003e\n\u003cli\u003eAziz, M. A., Ayob, N. H., Ayob, N. A., Ahmad, Y., \u0026amp; Abdulsomad, K. (2024). Factors influencing farmer adoption of climate-smart agriculture technologies: Evidence from Malaysia. \u003cem\u003eHuman Technology, 20\u003c/em\u003e(1), 70\u0026ndash;92. https://doi.org/10.14254/1795-6889.2024.20-1.4\u003c/li\u003e\n\u003cli\u003eBhatti, M. A., Godfrey, S. S., Divon, S. A., Aamodt, J. T., \u0026Oslash;ystese, S., Wynn, P. C., Eik, L. O., \u0026amp; Fjeld-Solberg, \u0026Oslash;. (2022). Micro-investment by Tanzanian smallholders in drip irrigation kits for vegetable production to improve livelihoods: Lessons learned and a way forward. \u003cem\u003eAgriculture, 12\u003c/em\u003e(10), 1732. https://doi.org/10.3390/agriculture12101732\u003c/li\u003e\n\u003cli\u003eBongole, A. (2023). Adoption of multiple climate-smart agricultural practices in Mbeya and Songwe regions in Tanzania. \u003cem\u003eJournal of African Economic Perspectives, 1\u003c/em\u003e(1), 41\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eCGIAR. (2021). \u003cem\u003eClimate-smart agriculture policy implementation.\u003c/em\u003e https://www.cgiar.org/impact/\u003c/li\u003e\n\u003cli\u003eEbrahimi, S. S., Lashgharara, F., Mirdamadi, S. M., \u0026amp; Najafabadi, M. O. (2023). Factors influencing climate change adaptation practices and their impacts on food security dimensions in horticultural crops evaluated using PLS-SEM analysis. \u003cem\u003eBulgarian Journal of Agricultural Science, 29\u003c/em\u003e(5), 978\u0026ndash;993.\u003c/li\u003e\n\u003cli\u003eFawehinmi, O., Yusliza, M. Y., Tanveer, M. I., \u0026amp; Abdullahi, M. S. (2024). Influence of green human resource management on employee green behavior: The sequential mediating effect of perceived behavioral control and attitude toward corporate environmental policy. \u003cem\u003eCorporate Social Responsibility and Environmental Management, 31\u003c/em\u003e(3), 2514\u0026ndash;2536. https://doi.org/10.1002/csr.2707\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization (FAO). (2021). \u003cem\u003eFinancing climate-smart agriculture: A guide to investment models and business cases.\u003c/em\u003e https://www.fao.org/publications/card/en/c/CB8091EN/\u003c/li\u003e\n\u003cli\u003eInternational Fund for Agricultural Development (IFAD). (2019). \u003cem\u003eTanzania climate-smart agriculture program.\u003c/em\u003e https://www.ifad.org/en/web/operations/-/project/tanzania-climate-smart-agriculture\u003c/li\u003e\n\u003cli\u003eKebenei, P., Mutunga, W., \u0026amp; Wambua, J. (2023). The role of microfinance in climate adaptation among smallholder farmers. \u003cem\u003eAfrican Journal of Rural Development, 9\u003c/em\u003e(1), 78\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eLindawati, A. S. L., Handoko, B. L., \u0026amp; Mustapha, M. (2023). Planned behavior and social cognitive in predicting e-business intention to adopt eco-design. \u003cem\u003eACM International Conference Proceeding Series, 73\u003c/em\u003e(1), 79. https://doi.org/10.1145/3584816.3584827 \u003c/li\u003e\n\u003cli\u003eMang\u0026rsquo;ana, K. M., Hokororo, S. J., \u0026amp; Ndyetabula, D. W. (2024). An Investigation of the Extent of Implementation of the Financial Management Practices of Agri-SMEs in developing countries: Evidence from Tanzania. Sustainable Technology and Entrepreneurship, 3(1), 100049. https://doi.org/https://doi.org/10.1016/j.stae.2023.100049 \u003c/li\u003e\n\u003cli\u003eMa, W., \u0026amp; Rahut, D. B. (2024). Climate-smart agriculture: Adoption, impacts, and implications for sustainable development. \u003cem\u003eMitigation and Adaptation Strategies for Global Change, 29\u003c/em\u003e(5), 10139. https://doi.org/10.1007/s11027-024-10139-z\u003c/li\u003e\n\u003cli\u003eMeshesha, A. T., Birhanu, B. S., \u0026amp; Ayele, M. B. (2022). Effects of perceptions on adoption of climate-smart agriculture innovations: Empirical evidence from the upper Blue Nile Highlands of Ethiopia. \u003cem\u003eInternational Journal of Climate Change Strategies and Management, 14\u003c/em\u003e(3), 293\u0026ndash;311. https://doi.org/10.1108/IJCCSM-04-2021-0035\u003c/li\u003e\n\u003cli\u003eRoman Aschinger, S., Boillat, S., \u0026amp; Chinwe Ifejika Speranza, C. (2023). Smallholder livelihood resilience to climate variability in South-Eastern Kenya, 2012\u0026ndash;2015. \u003cem\u003eFrontiers in Sustainable Food Systems, 7\u003c/em\u003e, 15\u0026ndash;32. https://doi.org/10.3389/fsufs.2023.00099\u003c/li\u003e\n\u003cli\u003eSingh, R., Mir, M. A., \u0026amp; Nazki, A. A. (2024). Evaluation of tourist behavior towards traditional food consumption: Validation of extended Theory of Planned Behaviour. \u003cem\u003eCogent Social Sciences, 10\u003c/em\u003e(1), 2298893. https://doi.org/10.1080/23311886.2023.2298893\u003c/li\u003e\n\u003cli\u003eUnited Nations Environment Programme (UNEP). (2020). \u003cem\u003eClimate adaptation and resilience strategies in agriculture.\u003c/em\u003e https://www.unep.org/resources/publication\u003c/li\u003e\n\u003cli\u003eUnited Republic of Tanzania (URT). (2012). \u003cem\u003eNational Climate Change Strategy.\u003c/em\u003e https://www.tanzania.go.tz/\u003c/li\u003e\n\u003cli\u003eUnited Republic of Tanzania (URT). (2013). \u003cem\u003eNational Agriculture Policy.\u003c/em\u003e Ministry of Agriculture. https://www.kilimo.go.tz/uploads/NATIONAL_AGRICULTURAL_POLICY.pdf\u003c/li\u003e\n\u003cli\u003eUnited Republic of Tanzania (URT). (2017). \u003cem\u003eClimate-Smart Agriculture Guidelines.\u003c/em\u003e https://www.fao.org/3/i8757en/I8757EN.pdf\u003c/li\u003e\n\u003cli\u003eUnited States Agency for International Development (USAID). (2021). \u003cem\u003eAgriculture and market linkages in Tanzania.\u003c/em\u003e https://www.usaid.gov/tanzania/agriculture\u003c/li\u003e\n\u003cli\u003eWorld Bank. (2020). \u003cem\u003eDigital platforms for agricultural extension services in Africa.\u003c/em\u003e https://documents.worldbank.org/en/publication/documents-reports/documentdetail/JIM-03-2022-0122\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate-Smart Agriculture, Intensity of use, Leafy vegetables, Smallholder agripreneurs, Semi-arid Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6024538/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6024538/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the behavioral determinants influencing the adoption of climate-smart agriculture (CSA) practices among smallholder leafy vegetable agripreneurs in semi-arid Central Tanzania. Guided by the Theory of Planned Behavior (TPB), the study investigates the role of attitudes, subjective norms, and perceived behavioral control in shaping CSA adoption, incorporating perceived usefulness as a mediating factor. Using a cross-sectional survey of 385 agripreneurs from Dodoma and Singida regions, data were analyzed using Structural Equation Modeling (SEM) in Smart PLS 4. The findings reveal that attitudes significantly influence perceived usefulness and CSA adoption, indicating that farmers who recognize the benefits of CSA are more likely to adopt these practices. Subjective norms and perceived behavioral control also play a crucial role, emphasizing the influence of social networks and access to resources. Perceived usefulness strongly mediates the relationship between behavioral determinants and adoption, underscoring its role in translating positive perceptions into action. The study highlights key policy implications, including strengthening agricultural extension services, improving financial access, and leveraging social networks to enhance CSA adoption. Despite limitations related to its cross-sectional design and reliance on self-reported data, the study offers valuable insights for policymakers, researchers, and development organizations. Future research should adopt longitudinal approaches and integrate objective farm-level assessments to deepen understanding of CSA adoption dynamics.\u003c/p\u003e","manuscriptTitle":"Behavioral Determinants of Climate-Smart Agriculture Adoption among Smallholder Leafy Vegetable Agripreneurs in Semi-Arid Central Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 17:21:44","doi":"10.21203/rs.3.rs-6024538/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c82d4cb-034e-4fdf-a74a-de447b57bcaf","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T10:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 17:21:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6024538","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6024538","identity":"rs-6024538","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}